Machine prediction has provided a number of tangible benefits in the modern computing environment. For example, learning models, such as neural networks, can be trained to identify trends in data that can be leveraged for a variety of benefits. In addition, other forms of artificial intelligence, such as reinforcement learning models, can accomplish machine optimization based on how these models are configured. However, the effectiveness of artificial intelligence systems is still dependent on configuration and the manner in which these systems are deployed. This is particularly apparent when different forms of artificial intelligence models are stacked. For example, some implementations may result in poor prediction or optimization based on, for example, the training data used, the structure of an implemented neural network, the configuration of a reinforcement learning model, and/or a lack of synergy between the different stacked layers. Accordingly, an artificial intelligence system that can effectively stack different machine learning models to realize synergies between machine prediction and optimization can provide an improved computing tool.